The Experts below are selected from a list of 14733 Experts worldwide ranked by ideXlab platform

Kim Hua Tan - One of the best experts on this subject based on the ideXlab platform.

  • Operational Intelligence discovery and knowledge mapping approach in a supply network with uncertainty
    Journal of Manufacturing Technology Management, 2006
    Co-Authors: S.c.l. Koh, Kim Hua Tan
    Abstract:

    Purpose – The purpose of this research is to propose an approach for discovering Operational Intelligence and knowledge mapping in a supply network with uncertainty.Design/methodology/approach – Knowledge mapping and handbook techniques are used. TAPS software is used to model a supply network with uncertainty and to discover Operational Intelligence in a supply network.Findings – Knowledge management is inadequate for managing a supply network with uncertainty. Knowledge mapping is proposed, but it needs to be assisted by Operational Intelligence.Practical implications – iTAPS provides managers with an ability to visualise the Operational Intelligence for a given objective, and to identify the likely effects on implementing a particular tool or technique in a supply network.Originality/value – A new approach – called the “Intelligence handbook” is proposed to discover Operational Intelligence in order to map knowledge in a supply network with uncertainty.

  • Operational Intelligence discovery and knowledge‐mapping approach in a supply network with uncertainty
    Journal of Manufacturing Technology Management, 2006
    Co-Authors: S.c.l. Koh, Kim Hua Tan
    Abstract:

    Purpose – The purpose of this research is to propose an approach for discovering Operational Intelligence and knowledge mapping in a supply network with uncertainty.Design/methodology/approach – Knowledge mapping and handbook techniques are used. TAPS software is used to model a supply network with uncertainty and to discover Operational Intelligence in a supply network.Findings – Knowledge management is inadequate for managing a supply network with uncertainty. Knowledge mapping is proposed, but it needs to be assisted by Operational Intelligence.Practical implications – iTAPS provides managers with an ability to visualise the Operational Intelligence for a given objective, and to identify the likely effects on implementing a particular tool or technique in a supply network.Originality/value – A new approach – called the “Intelligence handbook” is proposed to discover Operational Intelligence in order to map knowledge in a supply network with uncertainty.

S.c.l. Koh - One of the best experts on this subject based on the ideXlab platform.

  • Operational Intelligence discovery and knowledge mapping approach in a supply network with uncertainty
    Journal of Manufacturing Technology Management, 2006
    Co-Authors: S.c.l. Koh, Kim Hua Tan
    Abstract:

    Purpose – The purpose of this research is to propose an approach for discovering Operational Intelligence and knowledge mapping in a supply network with uncertainty.Design/methodology/approach – Knowledge mapping and handbook techniques are used. TAPS software is used to model a supply network with uncertainty and to discover Operational Intelligence in a supply network.Findings – Knowledge management is inadequate for managing a supply network with uncertainty. Knowledge mapping is proposed, but it needs to be assisted by Operational Intelligence.Practical implications – iTAPS provides managers with an ability to visualise the Operational Intelligence for a given objective, and to identify the likely effects on implementing a particular tool or technique in a supply network.Originality/value – A new approach – called the “Intelligence handbook” is proposed to discover Operational Intelligence in order to map knowledge in a supply network with uncertainty.

  • Operational Intelligence discovery and knowledge‐mapping approach in a supply network with uncertainty
    Journal of Manufacturing Technology Management, 2006
    Co-Authors: S.c.l. Koh, Kim Hua Tan
    Abstract:

    Purpose – The purpose of this research is to propose an approach for discovering Operational Intelligence and knowledge mapping in a supply network with uncertainty.Design/methodology/approach – Knowledge mapping and handbook techniques are used. TAPS software is used to model a supply network with uncertainty and to discover Operational Intelligence in a supply network.Findings – Knowledge management is inadequate for managing a supply network with uncertainty. Knowledge mapping is proposed, but it needs to be assisted by Operational Intelligence.Practical implications – iTAPS provides managers with an ability to visualise the Operational Intelligence for a given objective, and to identify the likely effects on implementing a particular tool or technique in a supply network.Originality/value – A new approach – called the “Intelligence handbook” is proposed to discover Operational Intelligence in order to map knowledge in a supply network with uncertainty.

Victor Hwang - One of the best experts on this subject based on the ideXlab platform.

  • Revamping Spacecraft Operational Intelligence
    SpaceOps 2012 Conference, 2012
    Co-Authors: Victor Hwang
    Abstract:

    The EPOXI flight mission has been testing a new commercial system, Splunk, which employs data mining techniques to organize and present spacecraft telemetry data in a high-level manner. By abstracting away data-source specific details, Splunk unifies arbitrary data formats into one uniform system. This not only reduces the time and effort for retrieving relevant data, but it also increases Operational visibility by allowing a spacecraft team to correlate data across many different sources. Splunk's scalable architecture coupled with its graphing modules also provide a solid toolset for generating data visualizations and building real-time applications such as browser-based telemetry displays.

  • Revamping Spacecraft Operational Intelligence with Splunk
    2012
    Co-Authors: Victor Hwang
    Abstract:

    So what is Splunk? Instead of giving the technical details, which you can find online, I'll tell you what it did for me. Splunk slapped everything into one place, with one uniform format, and gave me the ability to forget about all these annoying details of where it is, how to parse it, and all that. Instead, I only need to interact with Splunk to find the data I need. This sounds simple and obvious, but it's surprising what you can do once you all of your data is indexed in one place. By having your data organized, querying becomes much easier. Let's say that I want to search telemetry for a sensor_name gtemp_1 h and to return all data that is at most five minutes old. And because Splunk can hook into a real ]time stream, this data will always be up-to-date. Extending the previous example, I can now aggregate all types of data into one view based in time. In this picture, I've got transaction logs, telemetry, and downlinked files all in one page, organized by time. Even though the raw data looks completely than this, I've defined interfaces that transform it into this uniform format. This gives me a more complete picture for the question what was the spacecraft doing at this particular time? And because querying data is simple, I can start with a big block of data and whiddle it down to what I need, rather than hunting around for the individual pieces of data that I need. When we have all the data we need, we can begin widdling down the data with Splunk's Unix-like search syntax. These three examples highlights my trial-and-error attempts to find large temperature changes. I begin by showing the first 5 temperatures, only to find that they're sorted chronologically, rather than from highest temperatures to lowest temperatures. The next line shows sorting temperatures by their values, but I find that that fs not really what I want either. I want to know the delta temperatures between readings. Looking through Splunk's user manual, I find the delta function, which lets me dynamically generate new information to use in my query. With that extra piece of information, I can now return only the telemetry readings where the temperature changed by at least 10. One other useful feature I'll mention is that all of these queries can be run through Splunk's API. So any scripting language you can think of can plug right in and make these queries. This gives us the ability to build a lot of new tools.

Michal Greguš - One of the best experts on this subject based on the ideXlab platform.

  • Real-Time High-Load Infrastructure Transaction Status Output Prediction Using Operational Intelligence and Big Data Technologies
    Electronics, 2020
    Co-Authors: Solomia Fedushko, Taras Ustyianovych, Michal Greguš
    Abstract:

    An approach to use Operational Intelligence with mathematical modeling and Machine Learning to solve industrial technology projects problems are very crucial for today’s IT (information technology) processes and operations, taking into account the exponential growth of information and the growing trend of Big Data-based projects. Monitoring and managing high-load data projects require new approaches to infrastructure, risk management, and data-driven decision support. Key difficulties that might arise when performing IT Operations are high error rates, unplanned downtimes, poor infrastructure KPIs and metrics. The methods used in the study include machine learning models, data preprocessing, missing data imputation, SRE (site reliability engineering) indicators computation, quantitative research, and a qualitative study of data project demands. A requirements analysis for the implementation of an Operational Intelligence solution with Machine learning capabilities has been conducted and represented in the study. A model based on machine learning algorithms for transaction status code and output predictions, in order to execute system load testing, risks identification and, to avoid downtimes, is developed. Metrics and indicators for determining infrastructure load are given in the paper to obtain Operational Intelligence and Site reliability insights. It turned out that data mining among the set of Operational Big Data simplifies the task of getting an understanding of what is happening with requests within the data acquisition pipeline and helps identify errors before a user faces them. Transaction tracing in a distributed environment has been enhanced using machine learning and mathematical modelling. Additionally, a step-by-step algorithm for applying the application monitoring solution in a data-based project, especially when it is dealing with Big Data is described and proposed within the study.

Mohammad Yamin - One of the best experts on this subject based on the ideXlab platform.

  • A distributed smart fusion framework based on hard and soft sensors
    International Journal of Information Technology, 2017
    Co-Authors: Girija Chetty, Mohammad Yamin
    Abstract:

    In this paper we propose a novel intelligent processing approach based on hard and soft sensor fusion for obtaining better actionable Intelligence from automatic computer based decision support systems. The proposed smart fusion framework with particular focus on combining heterogeneous, multimedia, multimodal real-time big data streams—from hard and soft smart phone sensors, allows synergistic fusion to be achieved, leading to better Operational Intelligence from the computer based decision support systems. The details of this framework implementation with a component based software platform—the msifStudio, and its evaluation for some of the use case application scenarios is presented here.